---
title: "DashBoard"
output:
flexdashboard::flex_dashboard:
orientation: rows
theme: default
source_code: embed
---
```{r setup, include=FALSE}
library(ggplot2)
library(flexdashboard)
library(dplyr)
library(plotly)
df <- read.table("Baseball2010.txt", header = T)
```
Comparing Salary
==========
Row
------------------------------------------
### Avg Team Salary for the American League
```{r}
valueBox(round(mean(df$Salary[df$League == 1]), 2), color = "pink")
```
### Average Team Salary (Mil$)
```{r}
gauge(
round(mean(df$Salary),
digits = 2
),
min = 0,
max = max(df$Salary)
)
```
### Avg Team Salary for the National League
```{r}
valueBox(round(mean(df$Salary[df$League == 0]), 2), color = "dodgerblue")
```
Row
------------------------------------------
### Team Salary for American League
```{r}
p <- df %>%
filter(League == 1) %>%
ggplot(aes(x = reorder(Team, Salary, decreasing = T), y = Salary)) +
geom_bar(stat = "identity", fill = "pink", alpha = 0.8) +
labs(
title = "Team Salary Comparison for the American League",
x = "Team",
y = "Total 2010 Team Salary ($Mil)"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
coord_cartesian(ylim = c(0, max(df$Salary)))
ggplotly(p)
```
### Team Salary for the National League
```{r}
p <- df %>%
filter(League == 0) %>%
ggplot(aes(x = reorder(Team, Salary, decreasing = T), y = Salary)) +
geom_bar(stat = "identity", fill = "dodgerblue", alpha = 0.5) +
labs(
title = "Team Salary Comparison for the National League",
x = "Team",
y = "Total 2010 Team Salary ($Mil)"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
coord_cartesian(ylim = c(0, max(df$Salary)))
ggplotly(p)
```
# Win Rate
Row
------------------------------------------
### Relationship between Wins and Batting Average
```{r}
p <- ggplot(df, aes(x = Wins, y = Batting)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(
title = "Relationship between Wins and Batting Average",
x = "Number of Wins",
y = "Batting Average"
)
ggplotly(p)
```
### Relationship between Wins and HR
```{r}
p <- ggplot(df, aes(x = Wins, y = HR)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(
title = "Relationship between Wins and HR",
x = "Number of Wins",
y = "Batting Average"
)
ggplotly(p)
```
### Relationship between Wins and Errors
```{r}
p <- ggplot(df, aes(x = Wins, y = Errors)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(
title = "Relationship between Wins and Errors",
x = "Number of Wins",
y = "Batting Average"
)
ggplotly(p)
```